RESUMO
Ferroptosis, a cell death pathway dependent on iron, has been shown in research to play a role in the development, advancement, and outlook of tumours through ferroptosis-related lncRNAs (FRLRs). However, the value of the FRLRs in bladder cancer (BLCA) has not been thoroughly investigated. This research project involved developing a predictive model using ten specific FRLRs (AC099850.4, AL731567.1, AL133415.1, AC021321.1, SPAG5-AS1, HMGA2-AS1, RBMS3-AS3, AC006160.1, AL583785.1, and AL662844.4) through univariate COX and LASSO regression techniques. The validation of this signature as a standalone predictor was confirmed in a group of 65 patients from the urology bladder tumour database at the First Affiliated Hospital of Wenzhou Medical University in Wenzhou, China. Patients were categorized based on their median risk score into either a low-risk group or a high-risk group. Enrichment analysis identified possible molecular mechanisms that could explain the variations in clinical outcomes observed in high-risk and low-risk groups. Moreover, we explored the correlation between FLPS and immunotherapy-related indicators. The ability of FLPS to forecast the effectiveness of immunotherapy was validated by the elevated levels of immune checkpoint genes (PD-L1, CTLA4, and PD-1) in the group at high risk. We also screened the crucial FRLR (HMGA2-AS1) through congruent expression and prognostic conditions and established a ceRNA network, indicating that HMGA2-AS1 may affect epithelial-mesenchymal transition by modulating the Wnt signalling pathway through the ceRNA mechanism. We identified the top five mRNAs (NFIB, NEGR1, JAZF1, JCAD, and ESM1) based on random forest algorithm and analysed the relationship between HMGA2-AS1, the top five mRNAs, and immunotherapy, and their interactions with drug sensitivities. Our results suggest that patients with BLCA have a greater sensitivity to four drugs (dasatinib, pazopanib, erismodegib and olaparib). Our study provides new insights into the TME, key signalling pathways, genome, and potential therapeutic targets of BLCA, with future guidance for immunotherapy and targeted precision drugs.
RESUMO
BACKGROUND: Exosomes play a crucial role in intercellular communication in clear cell renal cell carcinoma (ccRCC), while the long non-coding RNAs (lncRNAs) are implicated in tumorigenesis and progression. AIMS: The purpose of this study is to construction a exosomes-related lncRNA score and a ceRNA network to predict the response to immunotherapy and potential targeted drug in ccRCC. METHODS: Data of ccRCC patients were obtained from the TCGA database. Pearson correlation analysis was used to identify eExosomes-related lncRNAs (ERLRs) from Top10 exosomes-related genes that have been screened. The entire cohort was randomly divided into a training cohort and a validation cohort in equal scale. LASSO regression and multivariate cox regression was used to construct the ERLRs-based score. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and drug susceptibility between the high- and low-risk groups were also investigated. Finally, the relevant ceRNA network was constructed by machine learning to analyze their potential targets in immunotherapy and drug use of ccRCC patients. RESULTS: A score consisting of 4ERLRs was identified, and patients with higher ERLRs-based score tended to have a worse prognosis than those with lower ERLRs-based score. ROC curves and multivariate Cox regression analysis demonstrated that the score could be considered as a risk factor for prognosis in both training and validation cohorts. Moreover, patients with high scores are predisposed to experience poor overall survival, a larger prevalence of advanced stage (III-IV), a greater tumor mutational burden, a higher infiltration of immunosuppressive cells, and a greater likelihood of responding favorably to immunotherapy. The importance of EMX2OS was determined by mechanical learning, and the ceRNA network was constructed, and EMX2OS may be a potential therapeutic target, possibly exerting its function through the EMX2OS/hsa-miR-31-5p/TLN2 axis. CONCLUSIONS: Based on machine learning, a novel ERLRs-based score was constructed for predicting the survival of ccRCC patients. The ERLRs-based score is a promising potential independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics. Meanwhile, we screened out key lncRNAEMX2OS and identified the EMX2OS/hsa-miR-31-5p/TLN2 axis, which may provide new clues for the targeted therapy of ccRCC.
